Perioperative 3D transoesophageal echocardiography. Part 1: fundamental principles
Bibliographic record
Abstract
By reading this article, you should be able to: Discuss the technological progress that facilitated the development of perioperative three-dimensional transoesophageal echocardiography (3D-TOE).Illustrate the technical principles that govern the process of 3D image generation in echocardiography.Describe the technical limitations of 3D-TOE.Anticipate the future developments of 3D echocardiography and their clinical impact. Key pointsThree-dimensional transoesophageal echocardiography (3D-TOE) is a mature technology with multiple perioperative applications.Three-dimensional imaging uses volume-based rather than sector-based scanning (i.e.twodimensional imaging).Three-dimensional image generation relies on the acquisition of a dataset made of multiple twodimensional 'slices' of a predefined volume by using matrix array transducers.Processing of the dataset is a critical multistep process that allows the analytical interrogation and the display of the structure of interest in 3D.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".